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      <div class="title">prtScoreCost</div>
      <div class="helptext"><pre><!--helptext -->  <span class="helptopic">prtScoreCost</span>  Return the cost vector
 
     COST = <span class="helptopic">prtScoreCost</span>(DECSTATS,LABELS, COSTMAT) returns the cost vector
     COST for the decision statistics DECSTATS and the corresponding labels
     LABELS, according to the cost matrix COSTMAT. DECSTATS must be a Nx1
     vector of decision statistics. LABELS must be a Nx1 vector of binary
     class labels. COST must be a 2x2 matrix, where Cij is the cost of
     deciding i when the truth is j.
 
     [COST, PF, PD] = prtScoreRoc(DECSTATS,LABELS) returns the probability
     of false alarm PF, the probability of detection PD at the
     corresponding COST.
 
     Example:     
     TestDataSet = prtDataGenSpiral;       % Create some test and
     TrainingDataSet = prtDataGenSpiral;   % training data
     classifier = prtClassSvm;             % Create a classifier
     classifier = classifier.train(TrainingDataSet);    % Train
     classified = run(classifier, TestDataSet);     
     %  Compute the cost vector
     C = <span class="helptopic">prtScoreCost</span>(classified.getX, TestDataSet.getY, [1 .1; .1 1]);</pre></div><!--after help --><!--seeAlso--><div class="footerlinktitle">See also</div><div class="footerlink"> <a href="./prtScoreConfusionMatrix.html">prtScoreConfusionMatrix</a>, <a href="./prtScoreRmse.html">prtScoreRmse</a>,
    <a href="./prtScorePercentCorrect.html">prtScorePercentCorrect</a>
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